code: def panel(coeffs, n_inputs, ax, colour=None):
      """Plot a list of sets of coefficients in the complex plane for a 1- or 2-dimensional function.
  
      Args:
          coeffs (list[array[complex]]): A list of sets of Fourier coefficients. The shape of the
              coefficient arrays must all be either 1- or 2-dimensional, i.e.,
              each array should have shape ``(2d + 1,)``
              for the 1-dimensional case, or ``(2d + 1, 2d + 1)`` where ``d`` is the
              degree, i.e., the maximum frequency of present in the coefficients.
              Such an array may be the output of the numpy/scipy ``fft``/``fft2`` functions,
              or :func:`~.pennylane.fourier.coefficients`.
          n_inputs (int): The number of variables in the function.
          ax (array[matplotlib.axes._subplots.AxesSubplot]): Axis on which to plot. For
              1-dimensional data, length must be the number of frequencies. For 2-dimensional
              data, must be a grid that matches the dimensions of a single set of coefficients.
          colour (str): The outline colour of the points on the plot.
  
      Returns:
          array[matplotlib.axes.Axes]: The axes after plotting is complete.
  
      **Example**
  
      Suppose we have the following quantum function:
  
      .. code-block:: python
  
          dev = qml.device('default.qubit', wires=2)
  
          @qml.qnode(dev)
          def circuit_with_weights(w, x):
              qml.RX(x[0], wires=0)
              qml.RY(x[1], wires=1)
              qml.CNOT(wires=[1, 0])
  
              qml.Rot(*w[0], wires=0)
              qml.Rot(*w[1], wires=1)
              qml.CNOT(wires=[1, 0])
  
              qml.RX(x[0], wires=0)
              qml.RY(x[1], wires=1)
              qml.CNOT(wires=[1, 0])
  
              return qml.expval(qml.PauliZ(0))
  
      We would like to compute and plot the distribution of Fourier coefficients
      for many random values of the weights ``w``. First, we generate all the coefficients:
  
      .. code-block:: python
  
          from functools import partial
  
          coeffs = []
  
          n_inputs = 2
          degree = 2
  
          for _ in range(100):
              weights = np.random.normal(0, 1, size=(2, 3))
              c = coefficients(partial(circuit_with_weights, weights), n_inputs, degree)
              coeffs.append(c)
  
      We can now plot by setting up a pair of ``matplotlib`` axes and passing them
      to the plotting function. The of axes must be large enough to represent all
      the available coefficients (in this case, since we have 2 variables and use
      degree 2, we need a 5x5 grid.
  
      >>> import matplotlib.pyplot as plt
      >>> from pennylane.fourier.visualize import panel
      >>> fig, ax = plt.subplots(5, 5, figsize=(12, 10), sharex=True, sharey=True)
      >>> panel(coeffs, n_inputs, ax)
  
      .. image:: ../../_static/fourier_vis_panel.png
          :align: center
          :width: 800px
          :target: javascript:void(0);
  
      """
      if n_inputs in [1, 2]:
          coeffs = _validate_coefficients(coeffs, n_inputs, True)
      else:
          raise ValueError(
              "Panel plot function accepts input coefficients for only 1- or 2-dimensional functions."
          )
  
      if ax.shape != coeffs[0].shape:
          raise ValueError(
              "Shape of subplot axes must match the shape of the coefficient data."
          )
  
      if colour is None:
          colour = "tab:blue"
  
      # This could probably be more efficient.
      # Plot 1D case
      if n_inputs == 1:
          # Range is (0, ..., degree) for rfft, (0, ... degree, -degree, ..., -1) for fft
          n_freqs = coeffs.shape[1] // 2 + (coeffs.shape[1] % 2)
          frequency_range = list(range(n_freqs)) + list(
              range(-n_freqs + 1, 0)
          )
  
          for coeff in range(coeffs.shape[1]):
              ax[coeff].scatter(
                  coeffs[:, coeff].real,
                  coeffs[:, coeff].imag,
                  facecolor="white",
                  edgecolor=colour,
              )
              ax[coeff].set_title(
                  f"{frequency_range[coeff]}", fontsize=14
              )
              ax[coeff].grid(True)
              ax[coeff].set_aspect("equal")
  
      # Plot 2D case
      else:
          n_freqs = coeffs.shape[1] // 2 + (coeffs.shape[1] % 2)
  
          frequency_range = list(range(n_freqs)) + list(
              range(-n_freqs + 1, 0)
          )
  
          for coeff_1, coeff_2 in product(
              list(range(coeffs.shape[1])), list(range(coeffs.shape[2]))
          ):
              ax[coeff_1, coeff_2].scatter(
                  coeffs[:, coeff_1, coeff_2].real,
                  coeffs[:, coeff_1, coeff_2].imag,
                  facecolor="white",
                  edgecolor=colour,
              )
              ax[coeff_1, coeff_2].set_title(
                  f"{frequency_range[coeff_1]}, {frequency_range[coeff_2]}",
                  fontsize=14,
              )
              ax[coeff_1, coeff_2].grid(True)
              ax[coeff_1, coeff_2].set_aspect("equal")
  
      return ax
  
file_path: pennylane/pennylane/fourier/visualize.py
imports: from itertools import product
  import numpy
  from .utils import format_nvec
label_desc: Use image node_id 7 for calling a global function with example usage: panel(coeffs, n_inputs, ax, colour) and returns: ax